Orca: copy name duplicated (#5886)

* Orca: `copy` name duplicated

python/orca/src/bigdl/orca/automl/model/base_keras_model.py

@@ -17,7 +17,7 @@
 import numpy as np
 from bigdl.orca.automl.metrics import Evaluator
 import pickle
-from copy import copy
+import copy
 import tensorflow as tf
 from tensorflow.keras import backend as K
 import types
@@ -88,7 +88,7 @@ def update_config():
             update_config()
             self.build(config)
         else:
-            tmp_config = copy(self.config)
+            tmp_config = copy.copy(self.config)
             tmp_config.update(config)
             self._check_config(**tmp_config)
             self.config.update(config)

python/orca/src/bigdl/orca/automl/model/base_pytorch_model.py

@@ -22,7 +22,7 @@
 import pandas as pd
 import tempfile
 import os
-from copy import copy
+import copy
 
 from bigdl.orca.automl.model.abstract import BaseModel, ModelBuilder
 from bigdl.orca.automl.metrics import Evaluator
@@ -144,7 +144,7 @@ def update_config():
             update_config()
             self.build(config)
         else:
-            tmp_config = copy(self.config)
+            tmp_config = copy.copy(self.config)
             tmp_config.update(config)
             self._check_config(**tmp_config)
             self.config.update(config)

python/orca/src/bigdl/orca/learn/mxnet/mxnet_runner.py

@@ -21,7 +21,7 @@
 import ray._private.services
 import mxnet as mx
 from mxnet import gluon
-from copy import copy
+import copy
 from bigdl.orca.ray.utils import to_list
 from bigdl.dllib.utils.log4Error import *
 
@@ -84,7 +84,7 @@ def train(self, train_data, epochs=1, batch_size=32,
         """Train the model and update the model parameters."""
         stats = dict()
         if self.is_worker:
-            config = copy(self.config)
+            config = copy.copy(self.config)
             if "batch_size" not in config:
                 config["batch_size"] = batch_size
 

python/orca/src/bigdl/orca/learn/pytorch/callbacks/wandb.py

@@ -15,7 +15,7 @@
 #
 from bigdl.orca.learn.pytorch.callbacks import Callback
 from bigdl.dllib.utils.log4Error import invalidInputError
-from copy import copy
+import copy
 
 try:
     import wandb
@@ -86,7 +86,7 @@ def on_train_begin(self):
         else:
             config = {}
         if self.log_config:
-            trainer_config = copy(self.trainer.config)
+            trainer_config = copy.copy(self.trainer.config)
             config.update(trainer_config)
 
         if is_rank_zero:

python/orca/src/bigdl/orca/learn/pytorch/pytorch_pyspark_estimator.py

@@ -19,7 +19,7 @@
 import numbers
 import torch
 import numpy as np
-from copy import copy
+import copy
 
 from bigdl.orca.learn.pytorch.training_operator import TrainingOperator
 from bigdl.orca.learn.pytorch.pytorch_pyspark_worker import PytorchPysparkWorker
@@ -145,7 +145,7 @@ def __init__(
             training_operator_cls=training_operator_cls,
             scheduler_step_freq=scheduler_step_freq,
             use_tqdm=use_tqdm,
-            config=copy(self.config),
+            config=copy.copy(self.config),
             metrics=metrics,
             size=self.num_workers,
             cores_per_worker=self.cores_per_worker,

python/orca/src/bigdl/orca/learn/pytorch/pytorch_pyspark_worker.py

@@ -37,7 +37,7 @@
 from bigdl.orca.learn.utils import save_pkl
 import os
 import tempfile
-from copy import copy
+import copy
 
 from pyspark import BarrierTaskContext, TaskContext
 from bigdl.orca.learn.utils import save_pkl, duplicate_stdout_stderr_to_file, get_rank
@@ -161,7 +161,7 @@ def validate(self, data_creator, batch_size=32, num_steps=None, profile=False,
 
     def predict(self, data_creator, batch_size=32, profile=False):
         """Evaluates the model on the validation data set."""
-        config = copy(self.config)
+        config = copy.copy(self.config)
         self._toggle_profiling(profile=profile)
 
         partition = data_creator(config, batch_size)

python/orca/src/bigdl/orca/learn/pytorch/pytorch_ray_estimator.py

@@ -20,7 +20,7 @@
 import numbers
 import torch
 import numpy as np
-from copy import copy
+import copy
 
 from bigdl.orca.data.ray_xshards import RayXShards
 from bigdl.orca.learn.pytorch.training_operator import TrainingOperator
@@ -149,7 +149,7 @@ def __init__(
 
         self.initialization_hook = initialization_hook
         self.config = {} if config is None else config
-        worker_config = copy(self.config)
+        worker_config = copy.copy(self.config)
         params = dict(
             model_creator=self.model_creator,
             optimizer_creator=self.optimizer_creator,

python/orca/src/bigdl/orca/learn/pytorch/pytorch_ray_worker.py

@@ -29,7 +29,7 @@
 # limitations under the License.
 
 import ray
-from copy import copy
+import copy
 from bigdl.orca.learn.pytorch.utils import find_free_port
 from bigdl.orca.learn.pytorch.torch_runner import TorchRunner
 import torch.nn as nn
@@ -119,7 +119,7 @@ def setup_components_horovod(self):
 
     def predict(self, data_creator, batch_size=32, profile=False):
         """Evaluates the model on the validation data set."""
-        config = copy(self.config)
+        config = copy.copy(self.config)
         self._toggle_profiling(profile=profile)
 
         shards_ref = data_creator(config, batch_size)

python/orca/src/bigdl/orca/learn/pytorch/torch_runner.py

@@ -36,7 +36,7 @@
 import io
 import itertools
 import os
-from copy import copy
+import copy
 import tempfile
 import torch
 import torch.nn as nn
@@ -256,7 +256,7 @@ def should_wrap_dataloader(loader):
     def train_epochs(self, data_creator, epochs=1, batch_size=32, profile=False,
                      info=None, wrap_dataloader=None, callbacks=None,
                      validation_data_creator=None):
-        config = copy(self.config)
+        config = copy.copy(self.config)
         if OrcaContext.serialize_data_creator:
             with FileLock(
                     os.path.join(tempfile.gettempdir(), ".orcadata.lock")):
@@ -383,7 +383,7 @@ def train_epoch(self,
     def validate(self, data_creator, batch_size=32, num_steps=None, profile=False,
                  info=None, wrap_dataloader=None):
         """Evaluates the model on the validation data set."""
-        config = copy(self.config)
+        config = copy.copy(self.config)
         info = info or {}
         self._toggle_profiling(profile=profile)
 
@@ -412,7 +412,7 @@ def validate(self, data_creator, batch_size=32, num_steps=None, profile=False,
 
     def predict(self, partition, batch_size=32, profile=False):
         """Evaluates the model on the validation data set."""
-        config = copy(self.config)
+        config = copy.copy(self.config)
         self._toggle_profiling(profile=profile)
 
         params = {"batch_size": batch_size, "shuffle": False}

python/orca/src/bigdl/orca/learn/tf2/spark_runner.py

@@ -18,7 +18,7 @@
 import os
 import tempfile
 import shutil
-from copy import copy
+import copy
 
 import tensorflow as tf
 
@@ -319,7 +319,7 @@ def step(self, data_creator, epochs=1, batch_size=32, verbose=1,
         """
         Get model training results and new model.
         """
-        config = copy(self.config)
+        config = copy.copy(self.config)
         if data_config is not None:
             config.update(data_config)
         config["batch_size"] = batch_size
@@ -373,7 +373,7 @@ def validate(self, data_creator, batch_size=32, verbose=1, sample_weight=None,
         """
         Evaluates the model on the validation data set.
         """
-        config = copy(self.config)
+        config = copy.copy(self.config)
         if data_config is not None:
             config.update(data_config)
         config["batch_size"] = batch_size
@@ -431,7 +431,7 @@ def validate(self, data_creator, batch_size=32, verbose=1, sample_weight=None,
             return []
 
     def predict(self, data_creator, batch_size, verbose, steps, callbacks, data_config):
-        config = copy(self.config)
+        config = copy.copy(self.config)
         if data_config is not None:
             config.update(data_config)
 

python/orca/src/bigdl/orca/learn/tf2/tf_runner.py

@@ -35,7 +35,7 @@
 import shutil
 import tempfile
 import subprocess
-from copy import copy
+import copy
 
 import ray
 import numpy as np
@@ -335,7 +335,7 @@ def step(self, data_creator, epochs=1, batch_size=32, verbose=1,
              steps_per_epoch=None, validation_steps=None, validation_freq=1,
              data_config=None):
         """Runs a training epoch and updates the model parameters."""
-        config = copy(self.config)
+        config = copy.copy(self.config)
         if data_config is not None:
             config.update(data_config)
         config["batch_size"] = batch_size
@@ -385,7 +385,7 @@ def step(self, data_creator, epochs=1, batch_size=32, verbose=1,
     def validate(self, data_creator, batch_size=32, verbose=1, sample_weight=None,
                  steps=None, callbacks=None, data_config=None):
         """Evaluates the model on the validation data set."""
-        config = copy(self.config)
+        config = copy.copy(self.config)
         if data_config is not None:
             config.update(data_config)
         config["batch_size"] = batch_size
@@ -433,7 +433,7 @@ def validate(self, data_creator, batch_size=32, verbose=1, sample_weight=None,
         return [stats]
 
     def predict(self, data_creator, batch_size, verbose, steps, callbacks, data_config):
-        config = copy(self.config)
+        config = copy.copy(self.config)
         if data_config is not None:
             config.update(data_config)